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Iterative algorithms for solving generalized nonlinear mixed variational inequalities

Published: 01 December 2004 Publication History

Abstract

A new concept of g-partially relaxed strong monotonicity of mappings is introduced. By applying the auxiliary variational inequality technique, some new predictor-corrector iterative algorithms for solving generalized nonlinear mixed variational inequalities are suggested and analyzed. The convergence of the algorithms only need the continuity and the g-partially relaxed strongly monotonicity of mappings. These algorithms and convergence result are new, and generalize some known results in literature.

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Published In

cover image Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics  Volume 172, Issue 2
1 December 2004
195 pages

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 December 2004

Author Tags

  1. g-partially relaxed strongly monotone
  2. auxiliary variational principle
  3. generalized nonlinear mixed variational inequality
  4. predictor-corrector iterative algorithm

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